ADAPTIVE VRFT BASED ON MFAC FOR THE SPEED CONTROL OF PMDC MOTOR

Keywords

Abstract

The signiﬁcance of mathematical modelling in the classical control
theory cannot be denied. However, the nonlinear system modelling
is more complex than linear modelling and sometimes it is chal-
lenging to produce a nonlinear mathematical model of the system.
The proposed work is mainly focused on data-driven virtual refer-
ence feedback tuning (VRFT) control combined with a model free
adaptive control (MFAC) algorithm. The basic control structure of
the VRFT system uses a close loop model as a reference. However,
the input and output data model of the closed loop linear system
is linearized in tight format. The reference model output error and
the system expected output error are used as a control input. The
estimated value of the pseudo partial derivative (PPD) in the past
time is introduced to the new control law to improve the utilization
rate of PPD. The whole performance of the controller design is es-
sentially data driven and it does not demand any prior information
about the system model. The VRFT-based MFA control system
is applied to speed control of the permanent magnet DC motor in
the Simulink platform. Moreover, the simulation results show that
8.6% speed tracking error is reduced using the proposed control
algorithm as compared to VRFT and 4.7% is reduced as compared
to MFA-based algorithms.